Abstract
Mechanomyography (MMG) is an important kinesiological tool and potential communication pathway for individuals with disabilities. However, MMG is highly susceptible to contamination by motion artifact due to limb movement. A better understanding of the nature of this contamination and its effects on different sensing methods is required to inform robust MMG sensor design. Therefore, in this study, we recorded MMG from the extensor carpi ulnaris of six able-bodied participants using three different co-located condenser microphone and accelerometer pairings. Contractions at 30% MVC were recorded with and without a shaker-induced single-frequency forearm motion artifact delivered via a custom test rig. Using a signal-to-signal-plus-noise-ratio and the adaptive Neyman curve-based statistic, we found that microphone-derived MMG spectra were significantly less influenced by motion artifact than corresponding accelerometer-derived spectra (p⩽0.05). However, non-vanishing motion artifact harmonics were present in both spectra, suggesting that simple bandpass filtering may not remove artifact influences permeating into typical MMG bands of interest. Our results suggest that condenser microphones are preferred for MMG recordings when the mitigation of motion artifact effects is important.
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